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Glocalink Singapore invested in AI food fingerprinting technology ProfilePrint together with Leave a Nest Singapore Pte. Ltd.

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~ProfilePrint, bringing food safety and quality assurance with AI based food ingredient fingerprinting technology~

Glocalink Singapore is proud to announce the 8th investment is AI food fingerprinting technology ProfilePrint. Their originality comes from A.I. food fingerprint technology that powers its ProfilePrint SaaS platform to allow any untrained user within seconds to ascertain quality, taste profile and freshness of ingredients like an expert.

“ProfilePrint empowers any regular person to perform like food assessment experts and that is why we decided to locate the Enabling Village, a place where people with different abilities are accepted for who they are and be valued for their contributions, because we believe that our A.I. technology not only helps sharpen the competitive edge for our clients, but also promotes stronger community inclusiveness and social mobility. We work with PhDs, MBAs, graduates from Universities, Polytechnics and ITE vocational schools, as well as persons-with-disabilities and maintain a diverse and inclusive hiring policy.”

    Mr Alan Lai, Founder and CEO of ProfilePrint

ProfilePrint has joined TECH PLANTER DEMO DAY in Singapore 2019 and received Mitsui Chemicals Award. Since then we have been in discussion with ProfilePrint to explore the way to support their business development. Together with Leave a Nest Singapore, Glocalink Singapore will be able to further support ProfilePrint to expand their business beyond the Singapore Market, especially to Japan.

ProfilePrint was founded in 2018 by Alan Lai and Rehan Amarasuriya. It is a Singapore based food technology startup. Their technology is combining metabolomics, artificial intelligence and sensor technologies.  Thye successfully commercialised the solution via a cloud-based platform with a portable analyser.  By having portable devices,  this allows stakeholders in the ingredient supply chain, ranging from growers, collectors, wholesalers, manufacturers to retailers to predict quality profiles rapidly without human intervention nor destroying the sample. 

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